Choice of Competitors. Specifically, our goal is to validate that direct MAUC optimization techniques leverage better MAUC performance than other imbalanced learning methods. Motivated by this, the competitors include: a  Baseline that does not deal with the imbalance distribution; Over-sampling and Under-sampling Methods that tackle imbalance issues by re-sampling; and Imbalanced Loss Functions that tackle imbalance issues by reformulating the loss function. Moreover, we implement our framework with the squared loss, exponential loss, and hinge loss with the acceleration methods proposed in Sec.. Note that, for the sampling-based methods, we use the code from the python Lib Imbalanced-learn [1]} https://imbalanced-learn.readthedocs.io/en/stable/# to implement these competitors. The details are listed as follows:
